DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances

Recent advances in pre-trained language models have significantly improved neural response generation. However, existing methods usually view the dialogue context as a linear sequence of tokens and learn to generate the next word through token-level self-attention. Such token-level encoding hinders the exploration of discourse-level coherence among utterances. This paper presents DialogBERT, a novel conversational response generation model that enhances previous PLM-based dialogue models. DialogBERT employs a hierarchical Transformer architecture. To efficiently capture the discourse-level coherence among utterances, we propose two training objectives, including masked utterance regression and distributed utterance order ranking in analogy to the original BERT training. Experiments on three multi-turn conversation datasets show that our approach remarkably outperforms the baselines, such as BART and DialoGPT, in terms of quantitative evaluation. The human evaluation suggests that DialogBERT generates more coherent, informative, and human-like responses than the baselines with significant margins.Comment: Published as a conference paper at AAAI 202

Determinants Of Enforcement Action By The Financial Supervisory Service Of Korea From The Perspective Of Audit Firms

In this study, we examine the determinants of enforcement action by the Financial Supervisory Service of Korea from the perspective of audit firms. Enforcement action is an indication of audit failure. Both client- and audit firm-specific factors are involved in its occurrence. Most published studies of enforcement after audit failure focus on client characteristics because details about audit firms from financial statements and information about organizational structure are not publicly available. However, examining the issues surrounding enforcement from the perspective of audit firms may also be valuable in elucidating the potential determinants of audit failure resulting in enforcement action. Utilizing publicly available data from audit firms in South Korea, we identify several audit firm characteristics as determinants of enforcement action. The results of our empirical analysis reveal that the likelihood of audit failure is positively associated with the ratio of accounts receivable to total assets, the ratio of audit fees to total revenue, the ratio of partners to the total number of CPAs, CEO ownership, and age of audit firms. In addition, the likelihood of audit failure is negatively associated with ownership concentration and profitability. These associations are more pronounced in non-affiliated audit firms than affiliated audit firms. Several useful implications for regulators are described for improving audit quality by means of enforcement action

Continuous Decomposition of Granularity for Neural Paraphrase Generation

While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information. Prior work has shown that decomposing the levels of granularity~(e.g., word, phrase, or sentence) for input tokens has produced substantial improvements, suggesting the possibility of enhancing Transformers via more fine-grained modeling of granularity. In this work, we propose a continuous decomposition of granularity for neural paraphrase generation (C-DNPG). In order to efficiently incorporate granularity into sentence encoding, C-DNPG introduces a granularity-aware attention (GA-Attention) mechanism which extends the multi-head self-attention with: 1) a granularity head that automatically infers the hierarchical structure of a sentence by neurally estimating the granularity level of each input token; and 2) two novel attention masks, namely, granularity resonance and granularity scope, to efficiently encode granularity into attention. Experiments on two benchmarks, including Quora question pairs and Twitter URLs have shown that C-DNPG outperforms baseline models by a remarkable margin and achieves state-of-the-art results in terms of many metrics. Qualitative analysis reveals that C-DNPG indeed captures fine-grained levels of granularity with effectiveness.Comment: Accepted to be published in COLING 202

THE TYPE OF INFORMATION OVERLOAD AFFECTS ELECTRONIC KNOWLEDGE REPOSITORY CONTINUANCE

In the present competitive organizational environment more organizations are implementing knowledge management initiatives to gain strategic advantage. One such initiative is that of implementing electronic knowledge repositories (EKR) which often leads to a rapid increase in the quantity of information employees must process daily, raising concerns of employees being overloaded. This is especially true for current EKRs using distributive technology, enabling customizable individual workspaces which can result in loose knowledge structures. This paper identifies a new type of information overload (IO), extending the concept as occurring in both knowledge seekers and contributors and uses cognitive dissonance theory to provide evidence that IO can change employees\u27 perception of EKR usage. This research paper provides the first empirical evidence that overload has no sufficient affect on EKR continuance intention directly, but has a significant negative affect on the two main success measures: Perceived usefulness and satisfaction of the system

Analytical Modeling of Rheological Postbuckling Behavior of Wood-Based Composite Panels Under Cyclic Hygro-Loading

This study was conducted to develop analytical models to predict postbuckling behavior of woodbased composite panels under cyclic humidity conditions. Both the Rayleigh method and von Karman theory of nonlinear plate with imperfection were used to obtain a closed form solution to the hygrobuckling and postbuckling. In addition, mechano-sorptive creep effects were also taken into account for the derivation of analytical models. The closed-form solutions derived for both isotropic and orthotropic materials showed a good agreement with the experimental results in terms of the center deformation of hardboard, especially in the case of the edge movements. The unrecovery deformation was much greater at the first cycle and then decreased as the number of cyclic hygro-loading increased